ECG-Guided Pre-Screening of Family Members for Hypertrophic Cardiomyopathy
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Background
Current clinical guidelines recommend serial ECG and echocardiographic surveillance for first-degree relatives of probands with Hypertrophic Cardiomyopathy (HCM).
Objectives
To evaluate the accuracy and validity of ECG alone as a pre-screening tool for the diagnosis of HCM and to develop a random forest (RF) model for HCM phenotype prediction.
Method
Pediatric relatives of primary HCM probands attending the cardiomyopathy screening program at The Hospital for Sick Children were included from 1993 till 2025. Subjects were followed until last follow-up censored at phenotype conversion. ECGs were classified as normal or abnormal based on predefined parameters. Associations between binary ECG variables and HCM phenotype were assessed using Phi (φ) coefficient. A Random Forest classifier was developed using significant ECG variables (70:30 training:test split) and evaluated using precision, recall, specificity, negative predictive value, F1 score and AUROC. feature importance was assessed using SHAP analysis. Variables with impact of >5% were included in a simplified model which was evaluated by repeating performance metrics and externally validated in a healthy cohort.
Results
350 screened relatives (44% female, mean follow-up 6.8 ± 4.8 years) were included. At baseline 13% (46\350) were phenotype-positive for HCM. 9 subjects converted during the surveillance. Thirteen ECG variables were significantly associated with phenotype-positive HCM and were included in the full random forest model. Four variables had >5% impact (Left ventricular hypertrophy, right ventricular hypertrophy, T-wave inversion and ST-segment depression) and were included in a simplified model which maintained high specificity (93% vs 97%), negative predictive value (97% vs 93%) and AUROC (90% vs, 96%). The simplified model classified 83% subjects as phenotype-negative with eight being false-negative, all of whom developed an abnormal ECG in a mean of 1 year and none had an interim adverse cardiac event. The simplified model was evaluated in an independent healthy cohort of 153 school age subjects and correctly identified 98% as phenotype-negative with 100% NPV.
Conclusion
ECG abnormalities were strongly associated with phenotype-positive status. A simplified ECG-based random forest model using four ECG variables demonstrated high specificity and negative predictive value for identifying phenotype-negative subjects. If prospectively validated, this could reduce the need for concurrent echocardiographic screening by up to 83% per encounter, lowering screening burden and cost.
